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Lightweight and efficient neural network with SPSA attention for wheat ear detection

Authors :
Yan Dong
Yundong Liu
Haonan Kang
Chunlei Li
Pengcheng Liu
Zhoufeng Liu
Source :
PeerJ Computer Science, Vol 8, p e931 (2022)
Publication Year :
2022
Publisher :
PeerJ Inc., 2022.

Abstract

Advancements in deep neural networks have made remarkable leap-forwards in crop detection. However, the detection of wheat ears is an important yet challenging task due to the complex background, dense targets, and overlaps between wheat ears. Currently, many detectors have made significant progress in improving detection accuracy. However, some of them are not able to make a good balance between computational cost and precision to meet the needs of deployment in real world. To address these issues, a lightweight and efficient wheat ear detector with Shuffle Polarized Self-Attention (SPSA) is proposed in this paper. Specifically, we first utilize a lightweight backbone network with asymmetric convolution for effective feature extraction. Next, SPSA attention is given to adaptively select focused positions and produce a more discriminative representation of the features. This strategy introduces polarized self-attention to spatial dimension and channel dimension and adopts Shuffle Units to combine those two types of attention mechanisms effectively. Finally, the TanhExp activation function is adopted to accelerate the inference speed and reduce the training time, and CIOU loss is used as the border regression loss function to enhance the detection ability of occlusion and overlaps between targets. Experimental results on the Global Wheat Head Detection dataset show that our method achieves superior detection performance compared with other state-of-the-art approaches.

Details

Language :
English
ISSN :
23765992
Volume :
8
Database :
Directory of Open Access Journals
Journal :
PeerJ Computer Science
Publication Type :
Academic Journal
Accession number :
edsdoj.00bd000e15f4ec8b45f380ae4e0aec1
Document Type :
article
Full Text :
https://doi.org/10.7717/peerj-cs.931